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The ALARA Imaging System is indicated for use digital dental radiography system for using X-ray recording media (phosphor storage screens) for radiographic diagnostic intraoral and extraoral exposures providing interactive CRT retrieval, viewing and processing of stored computed radiographic images.

The ALARA Imaging System is digital dental radiography system for using X-ray recording media (phosphor storage screens) for radiographic diagnostic intraoral and extraoral dental images.
ALARA Imaging System is comprised of the following main components:

• Reusable phosphor storage screens (Denta-screens) with commercially available disposable storage screen covers;
• . The ALARA Scanner, an image reader/digitizer (laser scanner and optical reader);
• The ALARA Scanner/Workstation Communications Interface;
• The ALARA Imaging System Workstation, with a CRT monitor, a workstation computer (the image processor), a keyboard and mouse (operator interface), proprietary workstation software, a floppy drive, and an optical hard drive (or digital analog tape) that is either resident in or attached to the workstation computer (image storage devices);
• A compatible commercially available laser printer (hard copy device); .
• A commercially available light box used to "erase" the phosphor storage screen "eraser"; and
• . Any compatible commercially available mass storage device(s) as backup electronic archive device(s).

This submission focuses on the ALARA Imaging System, a digital dental radiography system. However, the provided text does not contain any information regarding specific acceptance criteria or a study demonstrating the device's adherence to such criteria in terms of diagnostic performance. The document primarily focuses on establishing substantial equivalence to predicate devices based on indications for use, design, and functional features, along with physical testing to meet product specifications.

Therefore, for aspects related to diagnostic performance and clinical study details (like sample size, ground truth, expert involvement, etc.), the information is not present in the provided text.

Here's a breakdown of what can be extracted based on your request, with explicit notes about missing information:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance

• Sample Size for Test Set: Not specified. The "physical testing" mentioned is likely related to technical specifications rather than diagnostic performance on a test set of patient images.
• Data Provenance (e.g., country of origin of the data, retrospective or prospective): Not specified.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Adjudication Method: Not specified.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• MRMC Study Done: No, not mentioned. This submission is for a digital radiography system, not an AI-based diagnostic tool that would typically involve human reader comparisons with and without AI assistance.
• Effect Size of Human Readers with/without AI: Not applicable, as no such study is indicated.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

• Standalone Performance: Not applicable in the context of this submission. The device is a digital imaging system, not an algorithmic diagnostic tool that would operate in a standalone diagnostic capacity. Its "performance" is implicitly tied to its ability to capture and display diagnostic quality images for human interpretation.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

• Type of Ground Truth: Not specified. If "physical testing" referred to image quality, the ground truth might be technical specifications or comparisons to predicate device image outputs. For diagnostic accuracy, it would typically involve expert readings, pathology reports, or follow-up outcomes, none of which are detailed here.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable. This device is an imaging system, not a machine learning model that requires a "training set" in the conventional sense.

9. How the ground truth for the training set was established

• How Ground Truth for Training Set was Established: Not applicable (see point 8).

In summary, the provided document focuses on regulatory approval through substantial equivalence based on physical characteristics and functional similarity to existing devices, rather than a detailed clinical performance study with specific diagnostic acceptance criteria.

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